NSGA-II

Examples

Standard

from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.solution import read_solutions
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()
problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')

max_evaluations = 25000
algorithm = NSGAII(
    problem=problem,
    population_size=100,
    offspring_population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max=max_evaluations)
)

algorithm.run()
front = algorithm.get_result()

Distributed

Warning

This requires some extra dependencies

from dask.distributed import Client
from distributed import LocalCluster

from examples.multiobjective.parallel.zdt1_modified import ZDT1Modified
from jmetal.algorithm.multiobjective.nsgaii import DistributedNSGAII
from jmetal.operator import PolynomialMutation, SBXCrossover
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1Modified()

# setup Dask client
client = Client(LocalCluster(n_workers=24))

ncores = sum(client.ncores().values())
print(f'{ncores} cores available')

# creates the algorithm
max_evaluations = 25000

algorithm = DistributedNSGAII(
    problem=problem,
    population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max=max_evaluations),
    number_of_cores=ncores,
    client=client
)

algorithm.run()
front = algorithm.get_result()

Dynamic

from jmetal.algorithm.multiobjective.nsgaii import DynamicNSGAII
from jmetal.operator import PolynomialMutation, SBXCrossover
from jmetal.problem.multiobjective.fda import FDA2
from jmetal.util.observable import TimeCounter
from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = FDA2()

time_counter = TimeCounter(delay=1)
time_counter.observable.register(problem)
time_counter.start()

max_evaluations = 25000
algorithm = DynamicNSGAII(
    problem=problem,
    population_size=100,
    offspring_population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max=max_evaluations)
)

algorithm.observable.register(observer=PlotFrontToFileObserver('front_vis'))
algorithm.observable.register(observer=WriteFrontToFileObserver('front_files'))

algorithm.run()

Preference point-based (gNSGA-II)

from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT2
from jmetal.util.solutions import read_solutions
from jmetal.util.solutions.comparator import GDominanceComparator
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT2()
problem.reference_front = read_solutions(filename='resources/reference_front/ZDT2.pf')

reference_point = [0.2, 0.5]

max_evaluations = 25000
algorithm = NSGAII(
    problem=problem,
    population_size=100,
    offspring_population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    dominance_comparator=GDominanceComparator(reference_point),
    termination_criterion=StoppingByEvaluations(max=max_evaluations)
)

algorithm.run()
front = algorithm.get_result()

API

class jmetal.algorithm.multiobjective.nsgaii.DistributedNSGAII(problem: jmetal.core.problem.Problem, population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, number_of_cores: int, client: distributed.client.Client, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]

Bases: jmetal.core.algorithm.Algorithm

create_initial_solutions() → List[S][source]

Creates the initial list of solutions of a metaheuristic.

evaluate(solutions: List[S]) → List[S][source]

Evaluates a solution list.

get_name() → str[source]
get_observable_data() → dict[source]

Get observable data, with the information that will be send to all observers each time.

get_result() → R[source]
init_progress() → None[source]

Initialize the algorithm.

run()[source]

Execute the algorithm.

step() → None[source]

Performs one iteration/step of the algorithm’s loop.

stopping_condition_is_met() → bool[source]

The stopping condition is met or not.

update_progress()[source]

Update the progress after each iteration.

class jmetal.algorithm.multiobjective.nsgaii.DynamicNSGAII(problem: jmetal.core.problem.DynamicProblem[~S][S], population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]

Bases: jmetal.algorithm.multiobjective.nsgaii.NSGAII, jmetal.core.algorithm.DynamicAlgorithm

restart()[source]
stopping_condition_is_met()[source]

The stopping condition is met or not.

update_progress()[source]

Update the progress after each iteration.

class jmetal.algorithm.multiobjective.nsgaii.NSGAII(problem: jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]

Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm

get_name() → str[source]
get_result() → R[source]
replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]

This method joins the current and offspring populations to produce the population of the next generation by applying the ranking and crowding distance selection.

Parameters
  • population – Parent population.

  • offspring_population – Offspring population.

Returns

New population after ranking and crowding distance selection is applied.

jmetal.algorithm.multiobjective.nsgaii.R = ~R
jmetal.algorithm.multiobjective.nsgaii.reproduction(mating_population: List[S], problem, crossover_operator, mutation_operator) → S[source]